R: Exploratory Data Analysis

Feeling overwhelmed by your data? Unsure of where to start? This course in Exploratory Data Analysis (EDA) provides practical tools and techniques to help you understand and make sense of complex datasets. EDA uses intuitive graphical and numerical methods to reveal hidden patterns, relationships, and trends within data—helping you identify insights worth pursuing before making assumptions or performing advanced statistical analyses. EDA is an essential first step for researchers, analysts, and decision-makers alike, as it offers a way to build intuition about your data and discover compelling avenues for deeper investigation.

General information

Duration	12 hours

Content

Introduction to Exploratory Data Analysis (EDA): Identifying patterns, detecting anomalies, and hypothesis generation
EDA of Categorical Data: Counts, proportions, and tables
EDA of Numerical Data: Marginal and conditional distributions
Numerical Summaries: Measures of centre and variability, outlier detection and data transformation

Requirements

Participants should be familiar with R, ideally they will have had experience using packages from the tidyverse (e.g. ggplot, dplyr, etc.). Basic knowledge of statistics is helpful.

Target audience

This course is designed for participants who work with data in research or education and want to develop a strong foundation in data exploration and analysis. The course is open to all students and employees of the University of Zurich. It is particularly suitable for students at the BSc/MSc-level.

Goals

This course aims to provide participants with a comprehensive overview of the most widely used visualisation and numerical techniques in Exploratory Data Analysis (EDA). By the end of the course, participants will be able to:

Recognise and select appropriate EDA techniques for various data types and analytical tasks.
Apply these techniques effectively using the R programming language to explore, summarise, and visualise data.
Gain insights into underlying patterns and trends in datasets, building a solid foundation for further statistical analysis and decision-making.

Preparation

Material will be provided during the course.

Teaching methods

This course combines concise presentations, live demonstrations in R and RStudio, and supervised, hands-on exercises to foster a “learning by doing” approach. Each demonstration and exercise is grounded in practical research questions, helping participants connect EDA techniques with real-world applications.

Dates

Code	Referents	Dates	Available seats	Place
FS25-AREDA-01	Pedraza Perez Fernando	13.06.2025 - 20.06.2025 (09:00 - 16:00 o'clock)		Universität Zürich Irchel	Course registration begins on 1 February for the spring semester and on 1 September for the autumn semester.